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<!-- ==================== CLASS DESCRIPTION ==================== -->
<h1 class="epydoc">Class PNN</h1><p class="nomargin-top"><span class="codelink"><a href="peach.nn.nnet-pysrc.html#PNN">source&nbsp;code</a></span></p>
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PNN is the implementation of Probabilistic Neural Network, a network used
for classification tasks

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          <td><span class="summary-sig"><a href="peach.nn.nnet.PNN-class.html#__init__" class="summary-sig-name">__init__</a>(<span class="summary-sig-arg">self</span>,
        <span class="summary-sig-arg">sigma</span>=<span class="summary-sig-default">0.1</span>)</span><br />
      Initializes the network.</td>
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            <span class="codelink"><a href="peach.nn.nnet-pysrc.html#PNN.__init__">source&nbsp;code</a></span>
            
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          <td><span class="summary-sig"><a href="peach.nn.nnet.PNN-class.html#_kernel" class="summary-sig-name" onclick="show_private();">_kernel</a>(<span class="summary-sig-arg">self</span>,
        <span class="summary-sig-arg">x1</span>,
        <span class="summary-sig-arg">x2</span>)</span><br />
      This method gives a measure of how well a training sample can represent
the position of evaluation (i.e. how well x1 can represent x2, or vice
versa). If the distance D between x1 and x2 is small, result becomes
big. For distance 0 (i.e. x1 == x2), result becomes one and the sample
point is the best representation of evaluation point.</td>
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          <td><span class="summary-sig"><a href="peach.nn.nnet.PNN-class.html#train" class="summary-sig-name">train</a>(<span class="summary-sig-arg">self</span>,
        <span class="summary-sig-arg">trainSet</span>)</span><br />
      Presents a training set to the network.</td>
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            <span class="codelink"><a href="peach.nn.nnet-pysrc.html#PNN.train">source&nbsp;code</a></span>
            
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          <td><span class="summary-sig"><a href="peach.nn.nnet.PNN-class.html#__call__" class="summary-sig-name">__call__</a>(<span class="summary-sig-arg">self</span>,
        <span class="summary-sig-arg">x</span>)</span><br />
      The method to classify the input <tt class="rst-docutils literal">x</tt> into one of trained category.</td>
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            <span class="codelink"><a href="peach.nn.nnet-pysrc.html#PNN.__call__">source&nbsp;code</a></span>
            
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    <p class="indent-wrapped-lines"><b>Inherited from <code>object</code></b>:
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<a name="__init__"></a>
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  <h3 class="epydoc"><span class="sig"><span class="sig-name">__init__</span>(<span class="sig-arg">self</span>,
        <span class="sig-arg">sigma</span>=<span class="sig-default">0.1</span>)</span>
    <br /><em class="fname">(Constructor)</em>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="peach.nn.nnet-pysrc.html#PNN.__init__">source&nbsp;code</a></span>&nbsp;
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  <p>Initializes the network.</p>
<p>Is not necessary to inform the training set size, PNN will do it by
itself in <tt class="rst-docutils literal">train</tt> method.</p>
  <dl class="fields">
    <dt>Parameters:</dt>
    <dd><ul class="nomargin-top">
        <li><strong class="pname"><code>sigma</code></strong> - A real number. This value determines the spread of probability
density function (i.e is the smoothness parameter). A great value
for sigma will result in a large spread gaussian and the sample
points will cover a wide range of inputs, while a small value will
create a limited spread gaussian and the sample points will cover a
small range of inputs</li>
    </ul></dd>
    <dt>Overrides:
        object.__init__
    </dt>
  </dl>
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<a name="_kernel"></a>
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  <h3 class="epydoc"><span class="sig"><span class="sig-name">_kernel</span>(<span class="sig-arg">self</span>,
        <span class="sig-arg">x1</span>,
        <span class="sig-arg">x2</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="peach.nn.nnet-pysrc.html#PNN._kernel">source&nbsp;code</a></span>&nbsp;
    </td>
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  <p>This method gives a measure of how well a training sample can represent
the position of evaluation (i.e. how well x1 can represent x2, or vice
versa). If the distance D between x1 and x2 is small, result becomes
big. For distance 0 (i.e. x1 == x2), result becomes one and the sample
point is the best representation of evaluation point.</p>
<p>In the probabilistic view, this method calculates the probability
distribution.</p>
  <dl class="fields">
  </dl>
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  <h3 class="epydoc"><span class="sig"><span class="sig-name">train</span>(<span class="sig-arg">self</span>,
        <span class="sig-arg">trainSet</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="peach.nn.nnet-pysrc.html#PNN.train">source&nbsp;code</a></span>&nbsp;
    </td>
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  <p>Presents a training set to the network.</p>
<p>This method uses the sample inputs to set the size of network.</p>
  <dl class="fields">
    <dt>Parameters:</dt>
    <dd><ul class="nomargin-top">
        <li><strong class="pname"><code>train_set</code></strong> - The training set is a list of examples. It can have any size. In
fact, the definition of the training set is open. Each element of
the training set, however, should be a two-tuple <tt class="rst-docutils literal">(x, d)</tt>, where
<tt class="rst-docutils literal">x</tt> is the input vector, and <tt class="rst-docutils literal">d</tt> is the desired response of the
network for this particular input, i.e the category of <tt class="rst-docutils literal">x</tt>
pattern.</li>
    </ul></dd>
  </dl>
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<a name="__call__"></a>
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  <h3 class="epydoc"><span class="sig"><span class="sig-name">__call__</span>(<span class="sig-arg">self</span>,
        <span class="sig-arg">x</span>)</span>
    <br /><em class="fname">(Call operator)</em>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="peach.nn.nnet-pysrc.html#PNN.__call__">source&nbsp;code</a></span>&nbsp;
    </td>
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  The method to classify the input <tt class="rst-rst-docutils literal rst-docutils literal">x</tt> into one of trained category.
  <dl class="fields">
    <dt>Parameters:</dt>
    <dd><ul class="nomargin-top">
        <li><strong class="pname"><code>x</code></strong> - The input vector to the network.</li>
    </ul></dd>
    <dt>Returns:</dt>
        <dd>The category that best represent the input vector.</dd>
  </dl>
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